AI-Based Medical Diagnosis System

Abstract

Early detection of diseases is a critical factor in improving patient survival rates and reducing healthcare costs. Traditional diagnostic processes usually involve multiple laboratory tests, clinical examinations, and specialist consultations, which may consume considerable time and resources. With the rapid development of artificial intelligence and machine learning technologies, automated medical diagnosis systems have become increasingly popular in modern healthcare. These systems assist healthcare professionals by analyzing large volumes of medical data and identifying patterns that may indicate the presence of diseases at an early stage.Machine learning techniques have demonstrated remarkable potential in predicting diseases using structured medical datasets containing patient information such as blood pressure, glucose level, cholesterol level, body mass index, and other clinical parameters. At the same time, deep learning algorithms have shown strong performance in analyzing medical images such as X-rays and microscopic blood smear images. These advancements have enabled the development of intelligent diagnostic systems capable of detecting diseases more efficiently and accurately.This research presents an AI-based multi-disease medical diagnosis system that integrates both machine learning and deep learning techniques within a unified web-based platform. The proposed system is designed to predict several major diseases including diabetes, heart disease, liver disease, kidney disease, breast cancer, pneumonia, and malaria. Machine learning models are used to analyze tabular clinical data, while deep learning convolutional neural networks are used for detecting diseases from medical.